Automated Feedback Improves Teachers’ Questioning Quality in Brick-and-Mortar Classrooms: Opportunities for Further Enhancement

Dorottya Demszky, Jing Liu, Heather C. Hill, Shyamoli Sanghi, Ariel Chung

Computers & Education, 2024

The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education

Paiheng Xu, Jing Liu, Nathan Jones, Julie Cohen, Wei Ai

arXiv, NAACL 2024, 2024

Empowering educators via language technology

Dorottya (Dora) Demszky, Jeffrey B. Bush, Sidney K. D’Mello, Jennifer Jacobs, Isabelle Hau, Heather Hill, Jing Liu, Susanna Loeb, Bethanie Maples, Kylie Peppler, Rhea Pokorny, Matthew Rascoff, Jenny Robinson, David Yeager, Laura Wentworth

2023

Improving Teachers’ Questioning Quality through Automated Feedback: A Mixed-Methods Randomized Controlled Trial in Brick-and-Mortar Classrooms

Dorottya Demszky, Jing Liu, Heather C. Hill, Shyamoli Sanghi, Ariel Chung

EdWorkingPaper, 2023, pp. 23-875

M-powering teachers: natural language processing powered feedback improves 1:1 instruction and student outcomes

Dorottya Demszky, Jing Liu

ACM Conference on Learning, ACM, app, Copenhagen Denmark, 2023 Jul, pp. 59-69

Can automated feedback improve teachers’ uptake of student ideas? Evidence from a randomized controlled trial in a large-scale online course

Dorottya Demszky, Jing Liu, Heather C. Hill, Dan Jurafsky, Chris Piech

Educational Evaluation and Policy Analysis, app, 2023 May